Trends in the public health significance, definitions of disease, and implications for prevention of Alzheimer’s disease

Efficient Pause Extraction and Encode Strategy for Alzheimer’s Disease Detection Using Only Acoustic Features from Spontaneous Speech

Clinical studies have shown that speech pauses can reflect the cognitive function differences between Alzheimer’s Disease (AD) and non-AD patients, while the value of pause information in AD detection has not been fully explored. Herein, we propose a speech pause feature extraction and encoding strategy for only acoustic-signal-based AD detection. First, a voice activity detection (VAD) method was constructed to detect pause/non-pause feature and encode it to binary pause sequences that are easier to calculate. Then, an ensemble machine-learning-based approach was proposed for the classification of AD from the participants’ spontaneous speech, based on the VAD Pause feature sequence and common acoustic feature sets (ComParE and eGeMAPS). The proposed pause feature sequence was verified in five machine-learning models. The validation data included two public challenge datasets (ADReSS and ADReSSo, English voice) and a local dataset (10 audio recordings containing five patients and five controls, Chinese voice). Results showed that the VAD Pause feature was more effective than common feature sets (ComParE: 6373 features and eGeMAPS: 88 features) for AD classification, and that the ensemble method improved the accuracy by more than 5% compared to several baseline methods (8% on the ADReSS dataset; 5.9% on the ADReSSo dataset). Moreover, the pause-sequence-based AD detection method could achieve 80% accuracy on the local dataset. Our study further demonstrated the potential of pause information in speech-based AD detection, and also contributed to a more accessible and general pause feature extraction and encoding method for AD detection.

Constructing brain functional network by Adversarial Temporal-Spatial Aligned Transformer for early AD analysis

Introduction

The brain functional network can describe the spontaneous activity of nerve cells and reveal the subtle abnormal changes associated with brain disease. It has been widely used for analyzing early Alzheimer's disease (AD) and exploring pathological mechanisms. However, the current methods of constructing functional connectivity networks from functional magnetic resonance imaging (fMRI) heavily depend on the software toolboxes, which may lead to errors in connection strength estimation and bad performance in disease analysis because of many subjective settings.

Methods

To solve this problem, in this paper, a novel Adversarial Temporal-Spatial Aligned Transformer (ATAT) model is proposed to automatically map 4D fMRI into functional connectivity network for early AD analysis. By incorporating the volume and location of anatomical brain regions, the region-guided feature learning network can roughly focus on local features for each brain region. Also, the spatial-temporal aligned transformer network is developed to adaptively adjust boundary features of adjacent regions and capture global functional connectivity patterns of distant regions. Furthermore, a multi-channel temporal discriminator is devised to distinguish the joint distributions of the multi-region time series from the generator and the real sample.

Results

Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) proved the effectiveness and superior performance of the proposed model in early AD prediction and progression analysis.

Discussion

To verify the reliability of the proposed model, the detected important ROIs are compared with clinical studies and show partial consistency. Furthermore, the most significant altered connectivity reflects the main characteristics associated with AD.

Conclusion

Generally, the proposed ATAT provides a new perspective in constructing functional connectivity networks and is able to evaluate the disease-related changing characteristics at different stages for neuroscience exploration and clinical disease analysis.

Protective effects of Populus tomentiglandulosa against cognitive impairment by regulating oxidative stress in an amyloid beta25–35-induced Alzheimer's disease mouse model

BACKGROUND/OBJECTIVES

Alzheimer's disease (AD) is one of the most representative neurodegenerative disease mainly caused by the excessive production of amyloid beta (Aβ). Several studies on the antioxidant activity and protective effects of Populus tomentiglandulosa (PT) against cerebral ischemia-induced neuronal damage have been reported. Based on this background, the present study investigated the protective effects of PT against cognitive impairment in AD.

MATERIALS/METHODS

We orally administered PT (50 and 100 mg/kg/day) for 14 days in an Aβ_25-35-induced mouse model and conducted behavioral experiments to test cognitive ability. In addition, we evaluated the levels of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) in serum and measured the production of lipid peroxide, nitric oxide (NO), and reactive oxygen species (ROS) in tissues.

RESULTS

PT treatment improved the space perceptive ability in the T-maze test, object cognitive ability in the novel object recognition test, and spatial learning/long-term memory in the Morris water-maze test. Moreover, the levels of AST and ALT were not significantly different among the groups, indicating that PT did not show liver toxicity. Furthermore, administration of PT significantly inhibited the production of lipid peroxide, NO, and ROS in the brain, liver, and kidney, suggesting that PT protected against oxidative stress.

CONCLUSIONS

Our study demonstrated that administration of PT improved Aβ_25–35-induced cognitive impairment by regulating oxidative stress. Therefore, we propose that PT could be used as a natural agent for AD improvement.

Research Progress on Mechanism of Neuroprotective Roles of Apelin-13 in Prevention and Treatment of Alzheimer's Disease

Alzheimer's disease (AD) is the most common type of dementia. Currently, more than 50 million people live with dementia worldwide, and this number is expected to increase. Some of the typical pathological changes of AD include amyloid plaque, hyperphosphorylation of tau protein, secretion of inflammatory mediators, and neuronal apoptosis. Apelin is a neuroprotective peptide that is widely expressed in the body. Among members of apelin family, apelin-13 is the most abundant with a high neuroprotective function. Apelin-13/angiotensin domain type 1 receptor-associated proteins (APJ) system regulates several physiological and pathophysiological cell activities, such as apoptosis, autophagy, synaptic plasticity, and neuroinflammation. It has also been shown to prevent AD development. This article reviews the research progress on the relationship between apelin-13 and AD to provide new ideas for prevention and treatment of AD.

Commonalities between Copper Neurotoxicity and Alzheimer's Disease

Alzheimer's disease, a highly prevalent form of dementia, targets neuron function beginning from the hippocampal region and expanding outwards. Alzheimer's disease is caused by elevated levels of heavy metals, such as lead, zinc, and copper. Copper is found in many areas of daily life, raising a concern as to how this metal and Alzheimer's disease are related. Previous studies have not identified the common pathways between excess copper and Alzheimer's disease etiology. Our review corroborates that both copper and Alzheimer's disease target the hippocampus, cerebral cortex, cerebellum, and brainstem, affecting motor skills and critical thinking. Additionally, Aβ plaque formation was analyzed beginning from synthesis at the APP parent protein site until Aβ plaque formation was completed. Structural changes were also noted. Further analysis revealed a relationship between amyloid-beta plaques and copper ion concentration. As copper ion levels increased, it bound to the Aβ monomer, expediting the plaque formation process, and furthering neurodegeneration. These conclusions can be utilized in the medical community to further research on the etiology of Alzheimer's disease and its relationships to copper and other metal-induced neurotoxicity.